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Journal of Network Intelligence 2023年第3期8卷 1019-1048页

作者： Liu, Chang Zhang, Longfei Huang, Tianyu Wu, Yufeng Zhang, Fuquan Lin, Qi Department of Computer Science and Technology Beijing Institute of Technology 100081 China Beijing Key Laboratory of Digital Performance and Simulation Technology 100081 China College of Computer and Control Engineering Minjiang University Fuzhou350108 China Digital Media Art Key Laboratory of Sichuan Province Sichuan Conservatory of Music Chengdu610021 China Informatics King’s College London LondonWC2R 2LS United Kingdom

Intelligent performance creativity is a new research direction of the intersection of technology and art. At present, the cutting-edge technologies such as computer simulation, emotional computing and machine learning have been applied to the evaluation and enhancement of intelligent performance creativity. The application of these technologies has greatly increased the diversity of performance forms and the complexity of performance content. For the traditional stage performance, due to the lack of timely feedback from the audience, it is difficult for the director to enhance the creativity effec-tively. In order to solve this problem, this paper proposes concentration level enhancement method (CLEM). The CLEM extracts and analyzes the physiological and emotional characteristics of the audience by collecting the multimodal physiological signal data of the audience. Through the analysis of the features, the concentration information of the audience is obtained and the concentration level (CL) is defined. Based on the physiological and emotional induction mechanism, a performance creativity enhancement strategy is proposed, which enhances the performance creativity in two aspects: " Visual-based" and " Content-based". Through the concentration level enhancement method, compare the audience’s performance attention changes before and after the performance creativity enhancement, select the effective strategies to enhance the performance creativity, and provide the director with quantifiable performance creativity enhancement feedback results. The experimental results show that the CLEM proposed in this paper can effectively induce significant changes in the audience’s emotion, and the selected creative enhancement strategy can significantly improve the audience’s concentration and achieve the purpose of enhancing the creativity of the performance. © 2023, Journal of Network Intelligence.

关键词： Physiology

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Label-Free Biomolecule Detection with InP/AlGaAs Charge Plasma Dielectric-Modulated Vertical TFET Using TaN as Metal Gate

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IEEE Access 2025年 13卷 89667-89684页

作者： Elshafie, Hashim Alqahtani, Abdulrahman Saad Mubarakali, Azath Adarsh Rag, S. Venkatesh, M. Parthasarathy, P. King Khalid University College of Computer Science Department of Computer Engineering Abha61421 Saudi Arabia University of Bisha College of Computing and Information Technology Bisha67714 Saudi Arabia King Khalid University College of Computer Science Department of Informatics and Computer Systems Abha61421 Saudi Arabia Manipal Institute of Technology Department of Data Science and Computer Applications Manipal Academy of Higher Education Manipal576104 India CMR Institute of Technology Department of Electronics and Communication Engineering Karnataka Bengaluru560037 India

This study introduces a label-free biosensing method for biomolecule detection utilizing an InP/AlGaAs charge plasma dielectric-modulated vertical tunnel field-effect transistor (InP/AlGaAs VTFET) featuring TaN as the metal gate. The device comprises a nanocavity beneath the gate metal adjacent to the tunnelling junction, where biomolecules engage with the dielectric material, resulting in fluctuations in the drain current. A twin metal gate architecture with laterally split dielectrics is employed to eliminate short-channel effects. The biosensor has exceptional sensitivity, attaining a peak drain current sensitivity of 2.5 × 10 cm2 for biomolecules such as albumin (k = 7). The gadget proficiently identifies biomolecules with varying dielectric constants and charge distributions, enabling adaptable label-free detection of diverse targets. The study examines the influence of dielectric constant on critical metrics such as Energy Band Diagrams, Drain Current, Drain Sensitivity, and subthreshold swing (SS). The overlap between the source and pocket regions, along with the introduction of an auxiliary gate, optimizes the electrical characteristics. Simulation results show that the proposed InP/AlGaAs VTFET achieves a maximum sensitivity of 3.5 × 103, outperforming configurations without overlap (2 × 103), highlights the potential of proposed InP/AlGaAs VTFET for scalable, high-sensitivity, label-free biomolecule detection. © 2013 IEEE.

关键词： Junction gate field effect transistors

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Weapons Detection for Security and Video Surveillance Using CNN and YOLO-V5s

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computers, Materials & Continua 2022年第2期70卷 2761-2775页

作者： Abdul Hanan Ashraf Muhammad Imran Abdulrahman M.Qahtani Abdulmajeed Alsufyani Omar Almutiry Awais Mahmood Muhammad Attique Mohamed Habib Department of Computer Science Shaheed Zulfikar Ali Bhutto Institute of Science and TechnologyIslamabad44000Pakistan Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia College of Applied Computer Science King Saud University(Almuzahmiyah Campus)Riyadh11543Saudi Arabia Department of Computer Science HITEC UniversityTaxila47080Pakistan College of Computing and Informatics Saudi Electronic UniversityRiyadhSaudi Arabia Faculty of Engineering Port Said UniversityPort Fuad CityEgypt

In recent years,the number of Gun-related incidents has crossed over 250,000 per year and over 85%of the existing 1 billion firearms are in civilian hands,manual monitoring has not proven effective in detecting *** is why an automated weapon detection system is *** automated convolutional neural networks(CNN)weapon detection systems have been proposed in the past to generate good ***,These techniques have high computation overhead and are slow to provide real-time detection which is essential for the weapon detection *** models have a high rate of false negatives because they often fail to detect the guns due to the low quality and visibility issues of surveillance *** research work aims to minimize the rate of false negatives and false positives in weapon detection while keeping the speed of detection as a key *** proposed framework is based on You Only Look Once(YOLO)and Area of Interest(AOI).Initially,themodels take pre-processed frames where the background is removed by the use of the Gaussian blur *** proposed architecture will be assessed through various performance parameters such as False Negative,False Positive,precision,recall rate,and F1 *** results of this research work make it clear that due to YOLO-v5s high recall rate and speed of detection are *** reached 0.010 s per frame compared to the 0.17 s of the Faster *** is promising to be used in the field of security and weapon detection.

关键词： Video surveillance weapon detection you only look once convolutional neural networks

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Past, Present, and Future of CASS Educational Programs and Initiatives [Feature]

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IEEE Circuits and Systems Magazine 2024年第2期24卷 7-15页

作者： Rokhani, Fakhrul Zaman Zhang, Xinmiao Joshi, Rajiv V. Reis, Ricardo Grimblatt, Victor Tang, Kea-Tiong Li, Yongfu Amara, Amara Delgado-Restituto, Manuel Universiti Putra Malaysia Faculty of Engineering Department of Computer and Communication Systems Engineering Serdang43400 Malaysia The Ohio State University Department of Electrical and Computer Engineering ColumbusOH43210 United States IBM T. J. Watson Research Center Yorktown HeightsNY10598 United States Institute of Informatics Federal University of Rio Grande do Sul Porto Alegre90010-150 Brazil Synopsys California CA92037 United States National Tsing Hua University Department of Electrical Engineering Hsinchu300 Taiwan Shanghai Jiao Tong University Department of Micro and Nano Electronics Engineering Shanghai200240 China Paris75006 France Institute of Microelectronics of Sevilla Sevilla41092 Spain

The Circuits and Systems Society (CASS) places a strong emphasis on advancing education within its domain. Various educational programs and resources have contributed to the professional development of CASS members for more than 20 years now. A major transformation within the Society was started in 2021 with a clear vision to become a premier educational resource within circuits and systems areas of interest. This article provides a comprehensive review of flagship educational programs and past initiatives. Insights from the lessons learned and future trends in the education ecosystem guided society's strategies and frameworks for improving and developing new educational products and services. The benefits of learning with the CASS program are presented, highlighting the Society's deep commitment to serving the community at large in educational pursuit for years to come. © 2001-2012 IEEE.

关键词： engineering education

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Help! I need somebody. A Mapping Study about Expert Identification in Software Development 23

Help! I need somebody. A Mapping Study about Expert Identifi...

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37th Brazilian Symposium on Software engineering, SBES 2023, held in conjunction with the Brazilian Conference on Software: Theory and Practice, CBSoft 2023

作者： Braga, Carlos Santos Jr, Paulo Barcellos, Monalessa Computer Science Department Federal University of Espírito Santo ES Vitoria Brazil Leds Research Department of Informatics Federal Institute of Education Science and Technology of Espírito Santo ES Serra Brazil

ISBN: (纸本)9798400707872

Context: Software development is a knowledge-intensive activity, and its success in an organization relies deeply on knowledge sharing. Knowledge management challenges are often increased in agile environments, which involve a lot of tacit knowledge, commonly acquired through experiences and hard to be made explicit. Therefore, knowledge sharing among practitioners is crucial. However, identifying suitable experts to share specific knowledge is not trivial. It involves not only discovering the individuals with the desired knowledge but also considering other factors that may improve the expert responsiveness, such as social connections and availability. Objective: Considering the important role experts play in knowledge sharing, we decided to investigate approaches that help identify experts that can share knowledge in software development. Our goal is to provide a panorama of the existing approaches and shine a light on research opportunities. Method: We carried out a systematic literature mapping and analyzed 17 publications. Results: The results show that most approaches have relied on code repositories as a source of evidence for identifying experts and, consequently, focus on supporting developers and aiding in the codification activity. Additionally, expert identification has been mostly automated, and factors beyond possessing the desired knowledge have often been disregarded. Conclusion: Although there are several expert identification approaches, there has been a lack of concern with factors that influence reaching the most suitable expert for a specific situation (e.g., considering the characteristics of the person seeking knowledge). Moreover, there is a need for deeper reflection on how to better explore different artifacts as sources of expert evidence and how to combine them to improve expert identification. © 2023 ACM.

关键词： Knowledge management

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IPFS: An Off-Chain Storage Solution for Blockchain 5th

IPFS: An Off-Chain Storage Solution for Blockchain

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5th International Conference on Recent Innovations in Computing, ICRIC 2022

作者： Kaur, Manpreet Gupta, Shikha Kumar, Deepak Raboaca, Maria Simona Goyal, S.B. Verma, Chaman Department of Computer Science and Engineering Chandigarh University Mohali India Department of Computer Science and Engineering Guru Nanak Dev Engineering College Ludhiana India Apex Institute of Technology Chandigarh University Mohali India National Research and Development Institute for Cryogenic and Isotopic Technologies-ICSI Râmnicu Vâlcea Romania Faculty of Information Technology City University London Malaysia Department of Media and Educational Informatics Eötvös Loránd University Budapest Hungary

ISBN: (纸本)9789811998751

Due to intrinsic properties such as transparency, immutability, decentralization, and cryptographic security, blockchain has emerged as a revolutionary technology. Despite its widespread application, blockchain is not used to store huge data due to the restricted storage capacity offered by each block. Furthermore, because the data contained in a block must be duplicated on many other nodes on the network, storage space is wasted. Off-chain storage is therefore required to store significant volumes of data while preserving the efficiency and performance of the network. Additionally, off-chain solutions such as Inter-Planetary File System (IPFS) would reduce the cost requirements of blockchain nodes as data stored and processed by these nodes would be reduced. Hence, strong decentralized storage is provided by IPFS that is a P2P-based content-addressed file sharing system that uses cryptographic hashes to store data. As a result, by combining blockchain with IPFS, an efficient file sharing system would be created. This paper presents a comprehensive IPFS architecture, a simplified version of this integrated approach, and potential applications that might assist to maximize the worth of both systems. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Blockchain

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Automated Subject Allotment to Faculty in an academic institute Using Maxflow Problem

Automated Subject Allotment to Faculty in an Academic Instit...

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Advances in Computing research on Science engineering and technology (ACROSET), International Conference on

作者： Neha Sehta Naman Shah Department of Computer Science and Engineering Acropolis Institute of Technology and Research Indore India

ISBN: (数字)9798350388800

ISBN: (纸本)9798350388817

presently, educational organizations are utilizing manual procedures to assign subjects to faculties depending on their own preferences, which gives rise to prejudice and therefore, discontentment. The best approach would be to allocate the most qualified individual to instruct the appropriate course, taking into account their knowledge and experience. Many instructional units rely on manual trial-and-error methods, which are time-consuming, inefficient, and prone to biased judgment. Moreover, the distribution is likely disadvantageous for several academic personnel. This study introduces an automated allocation of subjects to the faculties, taking into account the preferences in an effective way. The subject allocation problem is mapped into a maxflow problem and solved using Edmonds-Karp algorithm. A satisfaction metric can be computed to evaluate the effectiveness of the solution proposed which is average calculated on the distribution of subject as per the choice of faculty.

关键词： Productivity Measurement Heuristic algorithms Scalability Manuals Organizations Resource management Personnel research and development Testing

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Missing Data Imputation: A Comprehensive Review

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Journal of computer and Communications 2024年第11期12卷 53-75页

作者： Majed Alwateer El-Sayed Atlam Mahmoud Mohammed Abd El-Raouf Osama A. Ghoneim Ibrahim Gad Department of Computer Science College of Computer Science and Engineering Taibah University Yanbu Saudi Arabia Computer Science Department Faculty of Science Tanta University Tanta Egypt Basic and Applied Science Institute College of Engineering and Technology Arab Academy for Science and Technology (AAST) Alexandria Egypt Department of Computer Science Faculty of Computers and informatics Tanta University Tanta Egypt

Missing data presents a significant challenge in statistical analysis and machine learning, often resulting in biased outcomes and diminished efficiency. This comprehensive review investigates various imputation techniques, categorizing them into three primary approaches: deterministic methods, probabilistic models, and machine learning algorithms. Traditional techniques, including mean or mode imputation, regression imputation, and last observation carried forward, are evaluated alongside more contemporary methods such as multiple imputation, expectation-maximization, and deep learning strategies. The strengths and limitations of each approach are outlined. Key considerations for selecting appropriate methods, based on data characteristics and research objectives, are discussed. The importance of evaluating imputation’s impact on subsequent analyses is emphasized. This synthesis of recent advancements and best practices provides researchers with a robust framework for effectively handling missing data, thereby improving the reliability of empirical findings across diverse disciplines.

关键词： Missing Data Machine Learning Prediction Deep Learning Imputation

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Enabling IoT Continuous Connectivity in Smart Spaces 22

Enabling IoT Continuous Connectivity in Smart Spaces

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22nd International Symposium on Parallel and Distributed Computing, ISPDC 2023

作者： Andreou, Andreas Mavromoustakis, Constandinos X. Batalla, Jordi Mongay Dobre, Ciprian Markakis, Evangelos Mastorakis, George University of Nicosia University of Nicosia Research Foundation Departement of Computer Science Nicosia Cyprus Warsaw University of Technology Warsaw Poland University Politehnica of Bucharest National Institute for Research and Development in Informatics Automatic Control and Computers Bucharest Romania Hellenic Mediterranean University Department of Electrical and Computer Engineering Heraklion Greece Hellenic Mediterranean University Department of Management Science and Technology Agios Nikolaos Greece

ISBN: (纸本)9798350341270

Smart spaces are a rapidly emerging concept in technology. They result from the convergence of various novel technologies, such as the Internet of Things, Machine Learning and Artificial Intelligence, which allow for greater levels of automation and control within physical environments. The devices which are connected to the IoT network are equipped with sensors to acquire and exchange data. As a result, the IoT has transformed how we live, work, and play. However, the deployment in smart spaces is not always the best due to the issues arising from network node positioning. Therefore, we are investigating solutions to this problem with a novel approach which utilises Voronoi diagrams in conjunction with the algorithmic genetic technique. First, the initial positions of the IoT nodes will be determined by simulating a homogeneous Poisson point process in the smart space environment. Then, after dividing the area into the Voronoi cells, the genetic algorithm will optimise the position towards achieving full network coverage within the smart space. Experimental results prove the 100% network coverage within the specified area. © 2023 IEEE.

关键词： Genetic algorithms

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Unleashing the Power of Big Data: A Comprehensive Analysis and Future Directions 6

Unleashing the Power of Big Data: A Comprehensive Analysis a...

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6th International Conference on Contemporary Computing and informatics, IC3I 2023

作者： Sharma, Annu Kumar, Gaurav Farooq, Mansoor Gupta, Manish Kumar Raj, Saranya Rai, Ashok Kumar RajaRajeshwari College of Engineering Department of Computer Science & Application Karnataka Bengaluru India Indian Institute of Science Education and Research Bhopal Department of Computer Science & Engineering Madhya Pradesh Bhopal India University of Kashmir Department of Management Studies Jammu & Kashmir India Madan Mohan Malaviya University of Technology Department of Information Technology & Computer Application UP Gorakhpur India Dronacharya Group of Institutions Department of Data Science & Engineering Uttar Pradesh Greater Noida India Madan Mohan Malaviya University of Technology Department of Computer Science & Engineering UP Gorakhpur India

ISBN: (纸本)9798350304480

Big Data (BD) has emerged as a transformative force, offering unprecedented opportunities for organizations to extract valuable insights and drive informed decision-making. This research paper presents a comprehensive analysis of the current state of BD, explores its potential applications across industries, and outlines future directions for maximizing its benefits. This paper examines the key components of BD, including data collection, storage, processing, and analytics. It investigates the technologies and methodologies that enable efficient handling of large volumes of structured and unstructured data. Additionally, it explores the impact of cloud computing, artificial intelligence (AI), and the Internet of Things (IoT) on the growth and utilization of BD. Overall, this research paper provides a comprehensive analysis of BD, its current applications, challenges, and future directions. It serves as a valuable resource for researchers, practitioners, and policymakers seeking to unlock the full power of BD and harness its benefits across diverse domains. © 2023 IEEE.

关键词： Analytics artificial intelligence Big data Internet of Things Processing

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